Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “geometric modelling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Constraining AGN Torus Sizes with Optical and Mid-infrared Ensemble Structure Functions

We propose a new method to constrain the size of the dusty torus in broad-line active galactic nuclei (AGNs) using optical and mid-infrared (MIR) ensemble structure functions (SFs). Because of the geometric dilution of the torus, the MIR response to optical continuum variations has suppressed variability with respect to the optical that depends on the geometry (e.g., size, orientation, opening angle) of the torus. More extended tori have steeper MIR SFs with respect to the optical SFs. We demonstrate the feasibility of this SF approach using simulated AGN light curves and a geometric torus model. While it is difficult to use SFs to constrain the orientation and opening angle due to the insensitivity of the SF on these parameters, the size of the torus can be well determined. Applying this method to the ensemble SFs measured for 587 SDSS quasars, we measure a torus R–L relation of $\mathrm{log}\,{R}_{\mathrm{eff}}(\mathrm{pc})={0.51}_{-0.04}^{+0.04}\times \mathrm{log}({{L}}_{\mathrm{bol}}/{10}^{46}\,\mathrm{erg}\ {{\rm{s}}}^{-1})-{0.38}_{-0.01}^{+0.01}$ in the WISE W1 band and sizes ~1.4 times larger in the W2 band, which are in good agreement with dust reverberation mapping measurements. Compared with the reverberation mapping technique, the SF method is much less demanding in data quality and can be applied to any optical+MIR light curves for which a lag measurement may not be possible, as long as the variability process and torus structure are stationary. While this SF method does not extract all information contained in the light curves (i.e., the transfer function), it provides an intuitive interpretation for the observed trends of AGN MIR SFs compared with optical SFs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Metaplectic geometrical optics for ray-based modeling of caustics: Theory and algorithms

The optimization of radio frequency-wave (RF) systems for fusion experiments is often performed using ray-tracing codes, which rely on the geometrical-optics (GO) approximation. However, GO fails at caustics such as cutoffs and focal points, erroneously predicting the wave intensity to be infinite. This is a critical shortcoming of GO, since the caustic wave intensity is often the quantity of interest, e.g., RF heating. Full-wave modeling can be used instead, but the computational cost limits the speed at which such optimizations can be performed. Here, we have developed a less expensive alternative called metaplectic geometrical optics (MGO). Instead of evolving waves in the usual x (coordinate) or k (spectral) representation, MGO uses a mixed X$\equiv$Ax+Bk representation. By continuously adjusting the matrix coefficients A and B along the rays, one can ensure that GO remains valid in the X coordinates without caustic singularities. The caustic-free result is then mapped back onto the original x space using metaplectic transforms. Here, we overview the MGO theory and review algorithms that will aid the development of an MGO-based ray-tracing code. We show how using orthosymplectic transformations leads to considerable simplifications compared to previously published MGO formulas. We also prove explicitly that MGO exactly reproduces standard GO when evaluated far from caustics (an important property that until now has only been inferred from numerical simulations), and we relate MGO to other semiclassical caustic-removal schemes published in the literature. Finally this discussion is then augmented by an explicit comparison of the computed spectrum for a wave bounded between two cutoffs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Deep learning approaches for instantaneous laser absorptance prediction in additive manufacturing

Abstract The quantification of absorbed light is essential for understanding laser-material interactions and melt pool dynamics in order to minimize defects in additively manufactured metal components. The geometry of a vapor depression formed during laser melting is closely related to laser energy absorption. This relationship has been observed by the state-of-the-art in situ high-speed synchrotron X-ray visualization and integrating sphere radiometry. These two techniques create a temporally resolved dataset consisting of vapor depression images and corresponding laser absorptance. In this work, we propose two different approaches to predict instantaneous laser absorptance. The end-to-end approach uses deep convolutional neural networks to learn implicit features of X-ray images automatically and predict the laser energy absorptance. The two-stage approach uses a semantic segmentation model to engineer geometric features and predict absorptance using classical regression models. While having distinct advantages, both approaches achieved a consistently low mean absolute error of less than 3.3%.

Chemistry↗
DOE OSTI2025

Kinetic analyses for solid-state phase transition of metastable amorphous-AlO x (2.5 < x ≤ 3.0) nanostructures into crystalline alumina polymorphs

Solid-solid phase change materials (SS-PCMs) hold promise for energy storage/dissipation in batteries and energetic materials. Yet, phase change kinetics for SS-PCMs undergoing metastable to semi-stable/stable phase transformations remain relatively ill-studied because trapping metastable phases remain challenging. Recently, we demonstrated the kinetic entrapment and stabilization of a highly disordered and amorphous Al-oxide phase m-AlO x @C (x~2.5-3.0) via laser ablation synthesis in solution (LASiS). We report here, to our knowledge, the first chemical kinetics analysis for S-S phase transition of the m-AlO 3 @C nanocomposites (< 5–8 nm sizes) into semi-stable equilibrium alumina phases (θ/γ-Al 2 O 3 ) via disproportionation reaction, while releasing excess trapped gases. Our results indicate the atomic density of the AlO 3 structures to be ~5–10 times less than that of the final Al 2 O 3 phases, which led to the hypothesis of a volume shrinkage process during their phase transition. Temperature-dependent X-ray diffraction studies reveal the high-temperature phase transition for m-AlO 3 → θ/γ-Al 2 O 3 to follow contracting volume kinetics model, thereby validating our earlier hypothesis. Using the geometric volume contraction model, reaction kinetics analyses from Arrhenius plots reveal the activation energy barrier for the phase transition to be ~270±11 kJ/mol. This makes the activation energy barrier nearly identical to the oxidation of micron-sized Al particles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

25 - ENERGY STORAGE↗
DOE OSTI2025

Investigating the Relationship Between Bolide Entry Angle and Apparent Direction of Infrasound Signal Arrivals

Infrasound sensing offers critical capabilities for detecting and geolocating bolide events globally. However, the observed back azimuths, directions from which infrasound signals arrive at stations, often differ from the theoretical expectations based on the bolide’s peak brightness location. For objects with shallow entry angles, which traverse longer atmospheric paths, acoustic energy may be emitted from multiple points along the trajectory, leading to substantial variability in back azimuth residuals. This study investigates how the entry angle of energetic bolides affects the back azimuth deviations, independent of extrinsic factors such as atmospheric propagation, station noise, and signal processing methodologies. A theoretical framework, the Bolide Infrasound Back-Azimuth EXplorer Model (BIBEX-M), was developed to compute predicted back azimuths solely from geometric considerations. The model quantifies how these residuals vary as a function of source-to-receiver distance, revealing that bolides entering at shallow angles, e.g., 10°, can produce average residuals of 20°, with deviations reaching up to 46° at distances below 1000 km, and remaining significant even at 5000 km (up to 8°). In contrast, bolides with steeper entry angles, e.g., > 60°, show smaller deviations, typically under 5° at 1000 km and diminishing to less than ~1° beyond 5000 km. These findings attest to the need for careful interpretation when evaluating signal detections and estimating bolide locations. This work is not only pertinent to bolides but also to other high-energy, extended-duration atmospheric phenomena such as space debris and reentry events, where similar geometric considerations can influence infrasound arrival directions.

Acoustics↗
DOE OSTI2024

Geometric Measures of Trustworthiness for Machine Learning Predictions

his report details the findings from the research and investigation of Geometric Measures of Trustworthiness for Machine Learning Predictions. We explored the trustworthiness of machine learning (ML) models’ predictions using geometric measures to quantify the similarity of a query point with the training data. Predictive uncertainty in ML can originate from at least three sources: (1) Model uncertainty, which represents the uncertainty in model form (e.g. decision tree, vs neural network) and estimating the model parameters from the training data, (2) Data uncertainty, which represents the natural complexities of the data such as class overlap and inherent noise, and (3) Distributional uncertainty, which represents the mismatch between the training and operational distributions. The proposed measures focus on measuring and explaining the data and distributional uncertainties by measuring the relationships of operational data with the training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗
DOE OSTI2022

Modeling of a Generic Pebble Bed High-temperature Gas-cooled Reactor (PB-HTGR) with SAM

This report presents the modeling of the core of a generic pebble-bed reactor (PBR) at the system level using the System Analysis Module (SAM) code. This work is an extension of a previous work by the authors (Ooi et al. (2021)) that used the so-called 2-D ring model approach to model the PBMR-400. With the new approach, the pebble bed of the reactor is modeled with multiple PBCoreChannel components with spherical heat structures which allows the code to calculate thermal fluid parameters with built-in closure relations. The new core-channel approach is an improvement to the 2-D ring model approach as it does not introduce geometric distortions to the model and thus reduces the uncertainties of the predictions. In addition to thermal fluid simulations, point kinetics (PKE) are included to the model. Simulations are performed under a steady-state normal operation condition and a load-following transient scenario. This particular transient scenario is chosen as it tests both the thermal fluid and neutronics aspects of the model. The predicted results from both the steady-state and transient scenarios are compared with the results by Stew- art et al. (2021) who performed similar simulations with a Griffin-Pronghorn coupled tool. Despite the differences between the codes, with SAM being a system-analysis code and Pronghorn being a porous-medium code, both sets of results compare favorably. The over- all profiles and trends of the predicted temperatures and reactivities from the SAM and Griffin/Pronghorn simulations are similar, with some differences in their predicted values. The first part of the report covers the significance of a relatively fast-running approach that is capable of modeling the pebble bed reactor at the system-level while simultaneously capturing the radial thermal behavior of the core. Then, the modeling approach used in this work is discussed in details. Lastly, the results and comparisons with the Griffin/Pronghorn simulation are presented.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2025

Raptor

Raptor is an efficient Python-based tool for predicting the formation and morphology of stochastic lack of fusion defects in metal AM processes. A major obstacle for the qualification and certification of additively manufactured parts in critical applications continues to be performance variability caused in part by porosity-related defects. High-fidelity process models that could predict these defect features are currently too computationally expensive for component-level analysis. To address this, Raptor employs a high-performance geometric method to model the dynamic melt pool rather than relying on computationally intensive thermal fluid dynamics. This allows Raptor to rapidly identify regions of unmelted material that correspond to lack of fusion pores. The efficiency of this approach significantly reduces the time and resources needed for generating 3D defect predictions, which enables users to conduct large-scale parameter studies and evaluate how process variations affect part quality. The framework offers operational flexibility; users can execute simulations through a simple command line interface or integrate core functions as a library within larger computational workflows. Simulation outputs include 3D porosity maps for visualization and tools for quantitative morphological analysis. These results are suitable for direct comparison with experimental characterization data from methods such as X-ray computed tomography and can be used for statistical process optimization.

Subraveti, Vamsi [Vanderbilt Univ., Nashville, TN ↗
DOE OSTI2025

Machine Learning for Predicting Multipactor Susceptibility in Planar RF Structures

Multipactor discharge is a persistent challenge in high-power microwave (HPM) and accelerator systems, where secondary electron avalanches can cause heating, vacuum degradation, and failure. This work presents the first supervised machine learning (ML) framework for multipactor prediction, trained on high-fidelity 3D Particle-in-Cell (PIC) simulation data in planar geometries. The model maps operational, geometric, and material-dependent secondary electron yield (SEY) parameters to the time-averaged electron growth rate, enabling rapid reconstruction of susceptibility charts. Among the models evaluated, tree-based ensemble methods such as Random Forest and Extra Trees demonstrate superior generalization to unseen materials compared to neural networks such as multilayer perceptron (MLP). Performance metrics, including Intersection over Union (IoU), Structural Similarity Index Measure (SSIM), and Pearson correlation, show close agreement with simulation benchmarks. Principal Component Analysis attributes generalization limits to material feature-space disjointedness.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Achieving geometric accuracy in FFT-based micromechanical models using conformal grid

Owing to its efficiency, simplicity and robustness, the FFT-based method has become the standard for computation of mechanical fields in a heterogeneous periodic unit cell. One of the main disadvantages of the FFT-based method is the inaccurate representation of the initial microstructure on a regular grid of voxels, which can be alleviated through the use of distorted initial grids. Here, in this paper, a method for generation of distorted initial grids conforming to the microstructural features (e.g. straight/curved boundaries) is proposed. The method determines the positions of the grid nodes in the initial configuration by solving a system of springs connecting the nodes. Microstructures consisting of layers, Voronoi tessellation and circular/spherical inclusions are considered, and mechanical fields simulated using the FFT-based method. It is found that distorted initial grids, conforming to the microstructural features, lead to more accurate mechanical fields in comparison to the corresponding non-distorted initial grid solution. The effect of initial grid distortion on the convergence of the FFT-based method is analyzed and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dendrite-resolved, full-melt-pool phase-field simulations to reveal non-steady-state effects and to test an approximate model

In this report we study the epitaxial, columnar growth of (multiply oriented) dendrites/cells for a spot melt in a polycrystalline Al–Cu substrate using two-dimensional, phase-field, direct numerical simulations (DNS) at the full-melt-pool scale. Our main objective is to compare the expensive DNS model to a much cheaper but approximate “line” model in which a single-crystal phase-field simulation is confined to a narrow rectangular geometry. To perform this comparison, we develop algorithms that automatically extract quantities of interest (QoIs) from both DNS and line models. These QoIs allow us to quantitatively assess the assumptions in the line model and help us analyze its discrepancy with the DNS model. We consider four sets of heat source parameters, mimicking welding and additive manufacturing conditions, that create a combination shallow and deep melt pools. Our largest DNS simulation used 16K x 14K grid points in space. Our main findings can be summarized as follows. Under AM conditions, the QoIs of line models are in excellent agreement with the full DNS results for both shallow and deep melt pools. Under welding conditions, the primary spacing of the DNS model is smaller than the prediction of line model. We identify a geometric crowding effect that accounts for the discrepancies between the DNS and line models. We propose two potential mechanisms that determine the response of the microstructure to geometric crowding.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Summary of Geometric Parameters and Their Effects on Performance of U-10Mo Fuel Plates

A monolithic plate-type fuel system has been under development to convert high-performance test reactors from highly enriched uranium to low-enrichment uranium fuels and is now moving into the qualification phase, a predecessor to the timely conversion of the target reactors. To qualify this fuel system, the plates must meet safety standards and perform well in a reactor. The plates must maintain mechanical integrity, exhibit geometric stability, and have stable and predictable in-reactor behavior. The requirement to maintain mechanical integrity under normal operating conditions is primarily demonstrated by successful testing. However, each high-performance reactor employs a distinct design, resulting in distinct plate geometries, with unique features, attributes, irregularities, and tolerances. Due to the abundance of such distinct geometric varieties, a single “generic” plate geometry capturing all extremes is not achievable. It is also impractical to test each of these proposed designs in a reactor. This limitation necessitates cautious evaluations since the thermomechanical response of a plate with a certain geometry may not be representative for a plate with a significantly different geometry. To address concerns related to in-reactor plate performance, large set of sensitivity studies were performed. These parametric studies aimed to better understand irradiation performance, while evaluating the sensitivity of results to various modeling inputs, including geometric, operational, and material parameters. This work studied selected geometric parameters based on provided fuel specifications and performed a series of parametric simulations. The resulting temperature, displacement, and stress strains were comparatively evaluated to determine the effects of various geometric parameters. This draft provides a “high-level summary” of our parametric sensitivity studies and summarizes the key findings.

42 ENGINEERING↗
DOE OSTI2024

Stabilized bases for high-order, interpolation semi-Lagrangian, element-based tracer transport

In a computational fluid model of the atmosphere, the advective transport of trace species, or tracers, can be computationally expensive. For efficiency, models often use semi-Lagrangian advection methods. High-order interpolation semi-Lagrangian (ISL) methods, in particular, can be extremely efficient, if the problem of property preservation specific to them can be addressed. Atmosphere models often use geometrically and logically nonuniform grids for efficiency and, as a result, element-based discretizations. Such grids and discretizations make stability a particular problem for ISL methods. Generally, high-order, element-based ISL methods that use the natural polynomial interpolant associated with a nodal finite-element discretization are unstable. Here, we derive new bases having order of accuracy up to nine, with positive nodal weights, that stabilize the element-based ISL method. We use these bases to construct the linear advection operator in the property-preserving Interpolation Semi-Lagrangian Element-based Transport (Islet) method. Then we discuss key software implementation details. Finally, we show performance results for the Energy Exascale Earth System Model's atmosphere dynamical core, comparing the original and new transport methods. These simulations used up to 27,600 Graphical Processing Units (GPU) on the Oak Ridge Leadership Computing Facility's Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modeling of Additively Manufactured Ceramic Heat Exchangers with Semi-Elliptical Cross-Section Flow Channels

An approximate and easily applied analytical model was developed for heat transfer calculations of heat exchangers consisting of multiple rows and columns of heat transfer fluid flow channels with semi-elliptical cross sections. Heat exchangers of this type are being developed by using ceramic material and additive manufacturing for high temperature and pressure-concentrating solar electric power plants. Calculations using the model require only the geometrical dimensions and flow conditions of the heat exchanger. Comparisons of modeling predictions, with both simulation results and experimental data, were conducted to verify the viability of the model. The results showed good agreement where almost all the modeling predictions were within 20% of the simulation results or the experimental data. Finally, the proposed modeling approach is more generally applicable to heat transfer analysis of heat exchangers with similar flow channel configurations to those considered in this study.

Ceramic↗
DOE OSTI2025

AQuaRef: machine learning accelerated quantum refinement of protein structures

Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical data, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. Here we present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 machine learned interatomic potential (MLIP) mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data. Notably, AQuaRef aids in determining proton positions, as illustrated in the challenging case of short hydrogen bonds in the parkinsonism-associated human protein DJ-1 and its bacterial homolog YajL.

Zubatyuk, Roman [Carnegie Mellon University, Pitts↗